Overview

Dataset statistics

Number of variables20
Number of observations42912
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory160.0 B

Variable types

Categorical7
Numeric13

Alerts

mes is highly overall correlated with mes_do_periodoHigh correlation
origem_lat is highly overall correlated with origem and 1 other fieldsHigh correlation
origem_long is highly overall correlated with origem and 1 other fieldsHigh correlation
destino_lat is highly overall correlated with destino and 1 other fieldsHigh correlation
destino_long is highly overall correlated with destino and 1 other fieldsHigh correlation
novos_casos is highly overall correlated with casos_ativos and 6 other fieldsHigh correlation
casos_ativos is highly overall correlated with novos_casos and 6 other fieldsHigh correlation
novas_mortes is highly overall correlated with novos_casos and 4 other fieldsHigh correlation
mortes_por_casos is highly overall correlated with novos_casos and 4 other fieldsHigh correlation
primeira_dose is highly overall correlated with novos_casos and 4 other fieldsHigh correlation
imunizados is highly overall correlated with novos_casos and 4 other fieldsHigh correlation
ano is highly overall correlated with novos_casos and 6 other fieldsHigh correlation
mes_do_periodo is highly overall correlated with mes and 7 other fieldsHigh correlation
origem is highly overall correlated with origem_lat and 2 other fieldsHigh correlation
origem_nome is highly overall correlated with origem_lat and 2 other fieldsHigh correlation
destino is highly overall correlated with destino_lat and 2 other fieldsHigh correlation
destino_nome is highly overall correlated with destino_lat and 2 other fieldsHigh correlation
novos_casos has 13791 (32.1%) zerosZeros
casos_ativos has 14792 (34.5%) zerosZeros
novas_mortes has 14792 (34.5%) zerosZeros
mortes_por_casos has 14792 (34.5%) zerosZeros
primeira_dose has 24607 (57.3%) zerosZeros
imunizados has 24607 (57.3%) zerosZeros

Reproduction

Analysis started2023-10-08 21:10:43.516384
Analysis finished2023-10-08 21:11:47.370116
Duration1 minute and 3.85 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ano
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
2019.0
12796 
2021.0
12527 
2020.0
11811 
2022.0
5778 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters257472
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019.0
2nd row2019.0
3rd row2019.0
4th row2019.0
5th row2019.0

Common Values

ValueCountFrequency (%)
2019.0 12796
29.8%
2021.0 12527
29.2%
2020.0 11811
27.5%
2022.0 5778
13.5%

Length

2023-10-08T21:11:47.589045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-08T21:11:48.091598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2019.0 12796
29.8%
2021.0 12527
29.2%
2020.0 11811
27.5%
2022.0 5778
13.5%

Most occurring characters

ValueCountFrequency (%)
0 97635
37.9%
2 78806
30.6%
. 42912
16.7%
1 25323
 
9.8%
9 12796
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 214560
83.3%
Other Punctuation 42912
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97635
45.5%
2 78806
36.7%
1 25323
 
11.8%
9 12796
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 42912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 257472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97635
37.9%
2 78806
30.6%
. 42912
16.7%
1 25323
 
9.8%
9 12796
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 257472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97635
37.9%
2 78806
30.6%
. 42912
16.7%
1 25323
 
9.8%
9 12796
 
5.0%

mes
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0518736
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:11:48.543638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4403825
Coefficient of variation (CV)0.56848221
Kurtosis-1.171736
Mean6.0518736
Median Absolute Deviation (MAD)3
Skewness0.18977775
Sum259698
Variance11.836231
MonotonicityNot monotonic
2023-10-08T21:11:49.026583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 4196
9.8%
1 4189
9.8%
3 4188
9.8%
6 4033
9.4%
5 4016
9.4%
4 3906
9.1%
8 3077
7.2%
11 3075
7.2%
10 3061
7.1%
12 3059
7.1%
Other values (2) 6112
14.2%
ValueCountFrequency (%)
1 4189
9.8%
2 4196
9.8%
3 4188
9.8%
4 3906
9.1%
5 4016
9.4%
6 4033
9.4%
7 3058
7.1%
8 3077
7.2%
9 3054
7.1%
10 3061
7.1%
ValueCountFrequency (%)
12 3059
7.1%
11 3075
7.2%
10 3061
7.1%
9 3054
7.1%
8 3077
7.2%
7 3058
7.1%
6 4033
9.4%
5 4016
9.4%
4 3906
9.1%
3 4188
9.8%

mes_do_periodo
Categorical

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
2019.0.2.0
 
1235
2019.0.3.0
 
1232
2019.0.1.0
 
1231
2021.0.7.0
 
1096
2021.0.6.0
 
1095
Other values (37)
37023 

Length

Max length11
Median length10
Mean length10.214276
Min length10

Characters and Unicode

Total characters438315
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019.0.1.0
2nd row2019.0.1.0
3rd row2019.0.1.0
4th row2019.0.1.0
5th row2019.0.1.0

Common Values

ValueCountFrequency (%)
2019.0.2.0 1235
 
2.9%
2019.0.3.0 1232
 
2.9%
2019.0.1.0 1231
 
2.9%
2021.0.7.0 1096
 
2.6%
2021.0.6.0 1095
 
2.6%
2021.0.5.0 1081
 
2.5%
2021.0.8.0 1071
 
2.5%
2021.0.11.0 1056
 
2.5%
2021.0.12.0 1052
 
2.5%
2021.0.10.0 1047
 
2.4%
Other values (32) 31716
73.9%

Length

2023-10-08T21:11:49.534944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019.0.2.0 1235
 
2.9%
2019.0.3.0 1232
 
2.9%
2019.0.1.0 1231
 
2.9%
2021.0.7.0 1096
 
2.6%
2021.0.6.0 1095
 
2.6%
2021.0.5.0 1081
 
2.5%
2021.0.8.0 1071
 
2.5%
2021.0.11.0 1056
 
2.5%
2021.0.12.0 1052
 
2.5%
2021.0.10.0 1047
 
2.4%
Other values (32) 31716
73.9%

Most occurring characters

ValueCountFrequency (%)
0 143608
32.8%
. 128736
29.4%
2 86061
19.6%
1 41782
 
9.5%
9 15850
 
3.6%
3 4188
 
1.0%
6 4033
 
0.9%
5 4016
 
0.9%
4 3906
 
0.9%
8 3077
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 309579
70.6%
Other Punctuation 128736
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 143608
46.4%
2 86061
27.8%
1 41782
 
13.5%
9 15850
 
5.1%
3 4188
 
1.4%
6 4033
 
1.3%
5 4016
 
1.3%
4 3906
 
1.3%
8 3077
 
1.0%
7 3058
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 128736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 438315
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 143608
32.8%
. 128736
29.4%
2 86061
19.6%
1 41782
 
9.5%
9 15850
 
3.6%
3 4188
 
1.0%
6 4033
 
0.9%
5 4016
 
0.9%
4 3906
 
0.9%
8 3077
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 438315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 143608
32.8%
. 128736
29.4%
2 86061
19.6%
1 41782
 
9.5%
9 15850
 
3.6%
3 4188
 
1.0%
6 4033
 
0.9%
5 4016
 
0.9%
4 3906
 
0.9%
8 3077
 
0.7%

origem
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
SBBR
 
2424
SBGO
 
2366
SBSV
 
2359
SBFZ
 
2351
SBCF
 
2345
Other values (14)
31067 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters171648
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSBBE
2nd rowSBBE
3rd rowSBBE
4th rowSBBE
5th rowSBBE

Common Values

ValueCountFrequency (%)
SBBR 2424
 
5.6%
SBGO 2366
 
5.5%
SBSV 2359
 
5.5%
SBFZ 2351
 
5.5%
SBCF 2345
 
5.5%
SBCT 2341
 
5.5%
SBPA 2331
 
5.4%
SBBE 2331
 
5.4%
SBFL 2285
 
5.3%
SBEG 2283
 
5.3%
Other values (9) 19496
45.4%

Length

2023-10-08T21:11:50.016323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sbbr 2424
 
5.6%
sbgo 2366
 
5.5%
sbsv 2359
 
5.5%
sbfz 2351
 
5.5%
sbcf 2345
 
5.5%
sbct 2341
 
5.5%
sbpa 2331
 
5.4%
sbbe 2331
 
5.4%
sbfl 2285
 
5.3%
sbeg 2283
 
5.3%
Other values (9) 19496
45.4%

Most occurring characters

ValueCountFrequency (%)
B 47667
27.8%
S 47373
27.6%
F 9262
 
5.4%
R 9134
 
5.3%
G 8997
 
5.2%
C 6967
 
4.1%
P 6132
 
3.6%
V 4641
 
2.7%
T 4623
 
2.7%
O 4622
 
2.7%
Other values (8) 22230
13.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 171648
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 47667
27.8%
S 47373
27.6%
F 9262
 
5.4%
R 9134
 
5.3%
G 8997
 
5.2%
C 6967
 
4.1%
P 6132
 
3.6%
V 4641
 
2.7%
T 4623
 
2.7%
O 4622
 
2.7%
Other values (8) 22230
13.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 171648
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 47667
27.8%
S 47373
27.6%
F 9262
 
5.4%
R 9134
 
5.3%
G 8997
 
5.2%
C 6967
 
4.1%
P 6132
 
3.6%
V 4641
 
2.7%
T 4623
 
2.7%
O 4622
 
2.7%
Other values (8) 22230
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 47667
27.8%
S 47373
27.6%
F 9262
 
5.4%
R 9134
 
5.3%
G 8997
 
5.2%
C 6967
 
4.1%
P 6132
 
3.6%
V 4641
 
2.7%
T 4623
 
2.7%
O 4622
 
2.7%
Other values (8) 22230
13.0%

origem_nome
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
BRASÍLIA
 
2424
GOIÂNIA
 
2366
SALVADOR
 
2359
FORTALEZA
 
2351
CONFINS
 
2345
Other values (14)
31067 

Length

Max length14
Median length12
Mean length8.101277
Min length5

Characters and Unicode

Total characters347642
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBELÉM
2nd rowBELÉM
3rd rowBELÉM
4th rowBELÉM
5th rowBELÉM

Common Values

ValueCountFrequency (%)
BRASÍLIA 2424
 
5.6%
GOIÂNIA 2366
 
5.5%
SALVADOR 2359
 
5.5%
FORTALEZA 2351
 
5.5%
CONFINS 2345
 
5.5%
CURITIBA 2341
 
5.5%
PORTO ALEGRE 2331
 
5.4%
BELÉM 2331
 
5.4%
FLORIANÓPOLIS 2285
 
5.3%
MANAUS 2283
 
5.3%
Other values (9) 19496
45.4%

Length

2023-10-08T21:11:50.442781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brasília 2424
 
4.9%
goiânia 2366
 
4.8%
salvador 2359
 
4.7%
fortaleza 2351
 
4.7%
confins 2345
 
4.7%
curitiba 2341
 
4.7%
porto 2331
 
4.7%
alegre 2331
 
4.7%
belém 2331
 
4.7%
florianópolis 2285
 
4.6%
Other values (12) 26273
52.8%

Most occurring characters

ValueCountFrequency (%)
A 51518
14.8%
I 36328
10.4%
O 33955
 
9.8%
R 27661
 
8.0%
E 22822
 
6.6%
L 20714
 
6.0%
N 19774
 
5.7%
S 17679
 
5.1%
C 15305
 
4.4%
U 11269
 
3.2%
Other values (16) 90617
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 340817
98.0%
Space Separator 6825
 
2.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 51518
15.1%
I 36328
10.7%
O 33955
10.0%
R 27661
 
8.1%
E 22822
 
6.7%
L 20714
 
6.1%
N 19774
 
5.8%
S 17679
 
5.2%
C 15305
 
4.5%
U 11269
 
3.3%
Other values (15) 83792
24.6%
Space Separator
ValueCountFrequency (%)
6825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 340817
98.0%
Common 6825
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 51518
15.1%
I 36328
10.7%
O 33955
10.0%
R 27661
 
8.1%
E 22822
 
6.7%
L 20714
 
6.1%
N 19774
 
5.8%
S 17679
 
5.2%
C 15305
 
4.5%
U 11269
 
3.3%
Other values (15) 83792
24.6%
Common
ValueCountFrequency (%)
6825
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335954
96.6%
None 11688
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 51518
15.3%
I 36328
10.8%
O 33955
10.1%
R 27661
 
8.2%
E 22822
 
6.8%
L 20714
 
6.2%
N 19774
 
5.9%
S 17679
 
5.3%
C 15305
 
4.6%
U 11269
 
3.4%
Other values (12) 78929
23.5%
None
ValueCountFrequency (%)
Ó 4567
39.1%
Í 2424
20.7%
 2366
20.2%
É 2331
19.9%

origem_lat
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-16.987046
Minimum-29.994722
Maximum-1.3847222
Zeros0
Zeros (%)0.0%
Negative42912
Negative (%)100.0%
Memory size335.4 KiB
2023-10-08T21:11:50.846387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-29.994722
5-th percentile-29.994722
Q1-23.435556
median-19.624444
Q3-9.5108333
95-th percentile-1.3847222
Maximum-1.3847222
Range28.61
Interquartile range (IQR)13.924723

Descriptive statistics

Standard deviation8.4136835
Coefficient of variation (CV)-0.49529998
Kurtosis-0.94178805
Mean-16.987046
Median Absolute Deviation (MAD)4.001667
Skewness0.43824175
Sum-728948.11
Variance70.79007
MonotonicityNot monotonic
2023-10-08T21:11:51.168777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-15.871111 2424
 
5.6%
-16.6325 2366
 
5.5%
-12.908611 2359
 
5.5%
-3.7758333 2351
 
5.5%
-19.624444 2345
 
5.5%
-25.531667 2341
 
5.5%
-29.994722 2331
 
5.4%
-1.3847222 2331
 
5.4%
-27.670278 2285
 
5.3%
-3.0411111 2283
 
5.3%
Other values (9) 19496
45.4%
ValueCountFrequency (%)
-29.994722 2331
5.4%
-27.670278 2285
5.3%
-25.531667 2341
5.5%
-23.626111 2102
4.9%
-23.435556 2182
5.1%
-23.006944 1699
4.0%
-22.91 2247
5.2%
-22.81 2166
5.0%
-20.258056 2282
5.3%
-19.624444 2345
5.5%
ValueCountFrequency (%)
-1.3847222 2331
5.4%
-3.0411111 2283
5.3%
-3.7758333 2351
5.5%
-8.1263889 2281
5.3%
-9.5108333 2256
5.3%
-12.908611 2359
5.5%
-15.65 2281
5.3%
-15.871111 2424
5.6%
-16.6325 2366
5.5%
-19.624444 2345
5.5%

origem_long
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-45.738442
Minimum-60.050556
Maximum-34.922778
Zeros0
Zeros (%)0.0%
Negative42912
Negative (%)100.0%
Memory size335.4 KiB
2023-10-08T21:11:51.418567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-60.050556
5-th percentile-60.050556
Q1-49.176111
median-46.656389
Q3-40.286389
95-th percentile-34.922778
Maximum-34.922778
Range25.127778
Interquartile range (IQR)8.889722

Descriptive statistics

Standard deviation6.3627338
Coefficient of variation (CV)-0.13911129
Kurtosis-0.23720088
Mean-45.738442
Median Absolute Deviation (MAD)3.405833
Skewness-0.2298665
Sum-1962728
Variance40.484381
MonotonicityNot monotonic
2023-10-08T21:11:51.668345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-47.918611 2424
 
5.6%
-49.221111 2366
 
5.5%
-38.3225 2359
 
5.5%
-38.532222 2351
 
5.5%
-43.971944 2345
 
5.5%
-49.176111 2341
 
5.5%
-51.171111 2331
 
5.4%
-48.478889 2331
 
5.4%
-48.5525 2285
 
5.3%
-60.050556 2283
 
5.3%
Other values (9) 19496
45.4%
ValueCountFrequency (%)
-60.050556 2283
5.3%
-56.1175 2281
5.3%
-51.171111 2331
5.4%
-49.221111 2366
5.5%
-49.176111 2341
5.5%
-48.5525 2285
5.3%
-48.478889 2331
5.4%
-47.918611 2424
5.6%
-47.134444 1699
4.0%
-46.656389 2102
4.9%
ValueCountFrequency (%)
-34.922778 2281
5.3%
-35.791667 2256
5.3%
-38.3225 2359
5.5%
-38.532222 2351
5.5%
-40.286389 2282
5.3%
-43.1625 2247
5.2%
-43.250556 2166
5.0%
-43.971944 2345
5.5%
-46.473056 2182
5.1%
-46.656389 2102
4.9%

destino
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
SBBR
 
2434
SBGO
 
2368
SBSV
 
2365
SBCF
 
2353
SBCT
 
2350
Other values (14)
31042 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters171648
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSBBR
2nd rowSBBR
3rd rowSBBR
4th rowSBBR
5th rowSBCF

Common Values

ValueCountFrequency (%)
SBBR 2434
 
5.7%
SBGO 2368
 
5.5%
SBSV 2365
 
5.5%
SBCF 2353
 
5.5%
SBCT 2350
 
5.5%
SBFZ 2343
 
5.5%
SBPA 2334
 
5.4%
SBBE 2326
 
5.4%
SBCY 2296
 
5.4%
SBRF 2286
 
5.3%
Other values (9) 19457
45.3%

Length

2023-10-08T21:11:51.926178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sbbr 2434
 
5.7%
sbgo 2368
 
5.5%
sbsv 2365
 
5.5%
sbcf 2353
 
5.5%
sbct 2350
 
5.5%
sbfz 2343
 
5.5%
sbpa 2334
 
5.4%
sbbe 2326
 
5.4%
sbcy 2296
 
5.4%
sbvt 2286
 
5.3%
Other values (9) 19457
45.3%

Most occurring characters

ValueCountFrequency (%)
B 47672
27.8%
S 47372
27.6%
F 9256
 
5.4%
R 9145
 
5.3%
G 9000
 
5.2%
C 6999
 
4.1%
P 6119
 
3.6%
V 4651
 
2.7%
T 4636
 
2.7%
E 4610
 
2.7%
Other values (8) 22188
12.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 171648
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 47672
27.8%
S 47372
27.6%
F 9256
 
5.4%
R 9145
 
5.3%
G 9000
 
5.2%
C 6999
 
4.1%
P 6119
 
3.6%
V 4651
 
2.7%
T 4636
 
2.7%
E 4610
 
2.7%
Other values (8) 22188
12.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 171648
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 47672
27.8%
S 47372
27.6%
F 9256
 
5.4%
R 9145
 
5.3%
G 9000
 
5.2%
C 6999
 
4.1%
P 6119
 
3.6%
V 4651
 
2.7%
T 4636
 
2.7%
E 4610
 
2.7%
Other values (8) 22188
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 47672
27.8%
S 47372
27.6%
F 9256
 
5.4%
R 9145
 
5.3%
G 9000
 
5.2%
C 6999
 
4.1%
P 6119
 
3.6%
V 4651
 
2.7%
T 4636
 
2.7%
E 4610
 
2.7%
Other values (8) 22188
12.9%

destino_nome
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
BRASÍLIA
 
2434
GOIÂNIA
 
2368
SALVADOR
 
2365
CONFINS
 
2353
CURITIBA
 
2350
Other values (14)
31042 

Length

Max length14
Median length12
Mean length8.0988302
Min length5

Characters and Unicode

Total characters347537
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRASÍLIA
2nd rowBRASÍLIA
3rd rowBRASÍLIA
4th rowBRASÍLIA
5th rowCONFINS

Common Values

ValueCountFrequency (%)
BRASÍLIA 2434
 
5.7%
GOIÂNIA 2368
 
5.5%
SALVADOR 2365
 
5.5%
CONFINS 2353
 
5.5%
CURITIBA 2350
 
5.5%
FORTALEZA 2343
 
5.5%
PORTO ALEGRE 2334
 
5.4%
BELÉM 2326
 
5.4%
CUIABA 2296
 
5.4%
RECIFE 2286
 
5.3%
Other values (9) 19457
45.3%

Length

2023-10-08T21:11:52.207045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brasília 2434
 
4.9%
goiânia 2368
 
4.8%
salvador 2365
 
4.8%
confins 2353
 
4.7%
curitiba 2350
 
4.7%
fortaleza 2343
 
4.7%
porto 2334
 
4.7%
alegre 2334
 
4.7%
belém 2326
 
4.7%
cuiaba 2296
 
4.6%
Other values (12) 26221
52.7%

Most occurring characters

ValueCountFrequency (%)
A 51521
14.8%
I 36334
10.5%
O 33902
 
9.8%
R 27670
 
8.0%
E 22790
 
6.6%
L 20698
 
6.0%
N 19751
 
5.7%
S 17681
 
5.1%
C 15311
 
4.4%
U 11302
 
3.3%
Other values (16) 90577
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 340725
98.0%
Space Separator 6812
 
2.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 51521
15.1%
I 36334
10.7%
O 33902
9.9%
R 27670
 
8.1%
E 22790
 
6.7%
L 20698
 
6.1%
N 19751
 
5.8%
S 17681
 
5.2%
C 15311
 
4.5%
U 11302
 
3.3%
Other values (15) 83765
24.6%
Space Separator
ValueCountFrequency (%)
6812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 340725
98.0%
Common 6812
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 51521
15.1%
I 36334
10.7%
O 33902
9.9%
R 27670
 
8.1%
E 22790
 
6.7%
L 20698
 
6.1%
N 19751
 
5.8%
S 17681
 
5.2%
C 15311
 
4.5%
U 11302
 
3.3%
Other values (15) 83765
24.6%
Common
ValueCountFrequency (%)
6812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335849
96.6%
None 11688
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 51521
15.3%
I 36334
10.8%
O 33902
10.1%
R 27670
 
8.2%
E 22790
 
6.8%
L 20698
 
6.2%
N 19751
 
5.9%
S 17681
 
5.3%
C 15311
 
4.6%
U 11302
 
3.4%
Other values (12) 78889
23.5%
None
ValueCountFrequency (%)
Ó 4560
39.0%
Í 2434
20.8%
 2368
20.3%
É 2326
19.9%

destino_lat
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-16.988636
Minimum-29.994722
Maximum-1.3847222
Zeros0
Zeros (%)0.0%
Negative42912
Negative (%)100.0%
Memory size335.4 KiB
2023-10-08T21:11:52.445875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-29.994722
5-th percentile-29.994722
Q1-23.435556
median-19.624444
Q3-9.5108333
95-th percentile-1.3847222
Maximum-1.3847222
Range28.61
Interquartile range (IQR)13.924723

Descriptive statistics

Standard deviation8.4086929
Coefficient of variation (CV)-0.49495985
Kurtosis-0.93916694
Mean-16.988636
Median Absolute Deviation (MAD)4.001667
Skewness0.43838437
Sum-729016.37
Variance70.706117
MonotonicityNot monotonic
2023-10-08T21:11:52.676001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-15.871111 2434
 
5.7%
-16.6325 2368
 
5.5%
-12.908611 2365
 
5.5%
-19.624444 2353
 
5.5%
-25.531667 2350
 
5.5%
-3.7758333 2343
 
5.5%
-29.994722 2334
 
5.4%
-1.3847222 2326
 
5.4%
-15.65 2296
 
5.4%
-8.1263889 2286
 
5.3%
Other values (9) 19457
45.3%
ValueCountFrequency (%)
-29.994722 2334
5.4%
-27.670278 2274
5.3%
-25.531667 2350
5.5%
-23.626111 2095
4.9%
-23.435556 2186
5.1%
-23.006944 1690
3.9%
-22.91 2239
5.2%
-22.81 2162
5.0%
-20.258056 2286
5.3%
-19.624444 2353
5.5%
ValueCountFrequency (%)
-1.3847222 2326
5.4%
-3.0411111 2284
5.3%
-3.7758333 2343
5.5%
-8.1263889 2286
5.3%
-9.5108333 2241
5.2%
-12.908611 2365
5.5%
-15.65 2296
5.4%
-15.871111 2434
5.7%
-16.6325 2368
5.5%
-19.624444 2353
5.5%

destino_long
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-45.745158
Minimum-60.050556
Maximum-34.922778
Zeros0
Zeros (%)0.0%
Negative42912
Negative (%)100.0%
Memory size335.4 KiB
2023-10-08T21:11:52.937028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-60.050556
5-th percentile-60.050556
Q1-49.176111
median-46.656389
Q3-40.286389
95-th percentile-34.922778
Maximum-34.922778
Range25.127778
Interquartile range (IQR)8.889722

Descriptive statistics

Standard deviation6.3646031
Coefficient of variation (CV)-0.13913173
Kurtosis-0.23942108
Mean-45.745158
Median Absolute Deviation (MAD)3.405833
Skewness-0.22910614
Sum-1963016.2
Variance40.508172
MonotonicityNot monotonic
2023-10-08T21:11:53.288676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-47.918611 2434
 
5.7%
-49.221111 2368
 
5.5%
-38.3225 2365
 
5.5%
-43.971944 2353
 
5.5%
-49.176111 2350
 
5.5%
-38.532222 2343
 
5.5%
-51.171111 2334
 
5.4%
-48.478889 2326
 
5.4%
-56.1175 2296
 
5.4%
-34.922778 2286
 
5.3%
Other values (9) 19457
45.3%
ValueCountFrequency (%)
-60.050556 2284
5.3%
-56.1175 2296
5.4%
-51.171111 2334
5.4%
-49.221111 2368
5.5%
-49.176111 2350
5.5%
-48.5525 2274
5.3%
-48.478889 2326
5.4%
-47.918611 2434
5.7%
-47.134444 1690
3.9%
-46.656389 2095
4.9%
ValueCountFrequency (%)
-34.922778 2286
5.3%
-35.791667 2241
5.2%
-38.3225 2365
5.5%
-38.532222 2343
5.5%
-40.286389 2286
5.3%
-43.1625 2239
5.2%
-43.250556 2162
5.0%
-43.971944 2353
5.5%
-46.473056 2186
5.1%
-46.656389 2095
4.9%

empresa
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
AZU
13823 
GLO
13783 
TAM
13396 
ONE
 
697
IPM
 
687
Other values (2)
 
526

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters128736
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZU
2nd rowGLO
3rd rowONE
4th rowTAM
5th rowAZU

Common Values

ValueCountFrequency (%)
AZU 13823
32.2%
GLO 13783
32.1%
TAM 13396
31.2%
ONE 697
 
1.6%
IPM 687
 
1.6%
PTB 495
 
1.2%
PAM 31
 
0.1%

Length

2023-10-08T21:11:53.685665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-08T21:11:54.215965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
azu 13823
32.2%
glo 13783
32.1%
tam 13396
31.2%
one 697
 
1.6%
ipm 687
 
1.6%
ptb 495
 
1.2%
pam 31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 27250
21.2%
O 14480
11.2%
M 14114
11.0%
T 13891
10.8%
Z 13823
10.7%
U 13823
10.7%
G 13783
10.7%
L 13783
10.7%
P 1213
 
0.9%
N 697
 
0.5%
Other values (3) 1879
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 128736
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27250
21.2%
O 14480
11.2%
M 14114
11.0%
T 13891
10.8%
Z 13823
10.7%
U 13823
10.7%
G 13783
10.7%
L 13783
10.7%
P 1213
 
0.9%
N 697
 
0.5%
Other values (3) 1879
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 128736
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27250
21.2%
O 14480
11.2%
M 14114
11.0%
T 13891
10.8%
Z 13823
10.7%
U 13823
10.7%
G 13783
10.7%
L 13783
10.7%
P 1213
 
0.9%
N 697
 
0.5%
Other values (3) 1879
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 27250
21.2%
O 14480
11.2%
M 14114
11.0%
T 13891
10.8%
Z 13823
10.7%
U 13823
10.7%
G 13783
10.7%
L 13783
10.7%
P 1213
 
0.9%
N 697
 
0.5%
Other values (3) 1879
 
1.5%

novos_casos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean755761.64
Minimum0
Maximum3322951
Zeros13791
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:11:54.651204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median500341
Q31338939
95-th percentile2204981
Maximum3322951
Range3322951
Interquartile range (IQR)1338939

Descriptive statistics

Standard deviation857685.33
Coefficient of variation (CV)1.1348622
Kurtosis0.96912261
Mean755761.64
Median Absolute Deviation (MAD)500341
Skewness1.1946108
Sum3.2431243 × 1010
Variance7.3562412 × 1011
MonotonicityNot monotonic
2023-10-08T21:11:55.152495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 13791
32.1%
1351564 1096
 
2.6%
2012110 1095
 
2.6%
1882511 1081
 
2.5%
862847 1071
 
2.5%
285485 1056
 
2.5%
191866 1052
 
2.5%
382172 1047
 
2.4%
647083 1040
 
2.4%
804116 1009
 
2.4%
Other values (20) 19574
45.6%
ValueCountFrequency (%)
0 13791
32.1%
2 1001
 
2.3%
5822 1001
 
2.3%
81297 950
 
2.2%
191866 1052
 
2.5%
285485 1056
 
2.5%
382172 1047
 
2.4%
428975 961
 
2.2%
500341 958
 
2.2%
570802 967
 
2.3%
ValueCountFrequency (%)
3322951 960
2.2%
3181005 963
2.2%
2204981 993
2.3%
2012110 1095
2.6%
1911396 996
2.3%
1882511 1081
2.5%
1527224 1000
2.3%
1351564 1096
2.6%
1349314 1000
2.3%
1338980 968
2.3%

casos_ativos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean727449.9
Minimum0
Maximum3583270.8
Zeros14792
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:11:55.597678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median450730.2
Q31259709.6
95-th percentile2662992.8
Maximum3583270.8
Range3583270.8
Interquartile range (IQR)1259709.6

Descriptive statistics

Standard deviation910672.64
Coefficient of variation (CV)1.2518699
Kurtosis1.9757361
Mean727449.9
Median Absolute Deviation (MAD)450730.2
Skewness1.5538446
Sum3.121633 × 1010
Variance8.2932466 × 1011
MonotonicityNot monotonic
2023-10-08T21:11:56.108267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 14792
34.5%
1563263.258 1096
 
2.6%
1590520.3 1095
 
2.6%
1381608.097 1081
 
2.5%
1259709.613 1071
 
2.5%
561836.6667 1056
 
2.5%
530924.3226 1052
 
2.5%
555439.8065 1047
 
2.4%
946714 1040
 
2.4%
410963.2333 1009
 
2.4%
Other values (19) 18573
43.3%
ValueCountFrequency (%)
0 14792
34.5%
989.7096774 1001
 
2.3%
27431.23333 950
 
2.2%
140364.2903 961
 
2.2%
381098.4839 999
 
2.3%
385547.0333 960
 
2.2%
410963.2333 1009
 
2.4%
450730.2 998
 
2.3%
520390.3871 947
 
2.2%
530924.3226 1052
 
2.5%
ValueCountFrequency (%)
3583270.767 968
2.3%
3479478.643 960
2.2%
2662992.839 967
2.3%
2191611.667 958
2.2%
2031708.903 962
2.2%
1590520.3 1095
2.6%
1563263.258 1096
2.6%
1454238.065 963
2.2%
1381608.097 1081
2.5%
1284922.067 996
2.3%

novas_mortes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15878.527
Minimum0
Maximum82364
Zeros14792
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:11:56.569097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6898
Q324073
95-th percentile58695
Maximum82364
Range82364
Interquartile range (IQR)24073

Descriptive statistics

Standard deviation20095.151
Coefficient of variation (CV)1.2655551
Kurtosis1.8698493
Mean15878.527
Median Absolute Deviation (MAD)6898
Skewness1.5320183
Sum6.8137935 × 108
Variance4.0381511 × 108
MonotonicityNot monotonic
2023-10-08T21:11:57.098368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 14792
34.5%
38168 1096
 
2.6%
55220 1095
 
2.6%
58695 1081
 
2.5%
24073 1071
 
2.5%
6898 1056
 
2.5%
4346 1052
 
2.5%
11046 1047
 
2.4%
16264 1040
 
2.4%
13295 1009
 
2.4%
Other values (19) 18573
43.3%
ValueCountFrequency (%)
0 14792
34.5%
202 1001
 
2.3%
3179 967
 
2.3%
3740 958
 
2.2%
4346 1052
 
2.5%
4740 968
 
2.3%
5778 950
 
2.2%
6898 1056
 
2.5%
8307 963
 
2.2%
10341 962
 
2.2%
ValueCountFrequency (%)
82364 996
2.3%
66919 993
2.3%
58695 1081
2.5%
55220 1095
2.6%
38168 1096
2.6%
32924 947
2.2%
30517 1000
2.3%
30416 960
2.2%
29561 1000
2.3%
28853 989
2.3%

mortes_por_casos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019277416
Minimum0
Maximum0.065608387
Zeros14792
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:11:57.619477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.024280714
Q30.027937097
95-th percentile0.049086667
Maximum0.065608387
Range0.065608387
Interquartile range (IQR)0.027937097

Descriptive statistics

Standard deviation0.016458173
Coefficient of variation (CV)0.85375406
Kurtosis0.031018625
Mean0.019277416
Median Absolute Deviation (MAD)0.0060479524
Skewness0.45429173
Sum827.2325
Variance0.00027087145
MonotonicityNot monotonic
2023-10-08T21:11:58.193059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 14792
34.5%
0.02795774194 1096
 
2.6%
0.02795366667 1095
 
2.6%
0.02785903226 1081
 
2.5%
0.02793709677 1071
 
2.5%
0.02783766667 1056
 
2.5%
0.02780354839 1052
 
2.5%
0.02786258065 1047
 
2.4%
0.027891 1040
 
2.4%
0.02820466667 1009
 
2.4%
Other values (19) 18573
43.3%
ValueCountFrequency (%)
0 14792
34.5%
0.009967419355 1001
 
2.3%
0.021143 968
 
2.3%
0.02165645161 967
 
2.3%
0.021895 958
 
2.2%
0.02226225806 962
 
2.2%
0.02324892857 960
 
2.2%
0.02428071429 1000
 
2.3%
0.02443354839 993
 
2.3%
0.02483483871 1000
 
2.3%
ValueCountFrequency (%)
0.0656083871 961
2.2%
0.05767966667 950
2.2%
0.04908666667 960
2.2%
0.03812645161 947
2.2%
0.03259774194 989
2.3%
0.03032866667 998
2.3%
0.02939580645 999
2.3%
0.02820466667 1009
2.4%
0.02795774194 1096
2.6%
0.02795366667 1095
2.6%

primeira_dose
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49270494
Minimum0
Maximum1.7970073 × 108
Zeros24607
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:11:58.626927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.3256941 × 108
95-th percentile1.7774267 × 108
Maximum1.7970073 × 108
Range1.7970073 × 108
Interquartile range (IQR)1.3256941 × 108

Descriptive statistics

Standard deviation70979696
Coefficient of variation (CV)1.4406126
Kurtosis-0.94318763
Mean49270494
Median Absolute Deviation (MAD)0
Skewness0.93954571
Sum2.1142954 × 1012
Variance5.0381172 × 1015
MonotonicityIncreasing
2023-10-08T21:11:59.853197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 24607
57.3%
102009213 1096
 
2.6%
75653853 1095
 
2.6%
47021991 1081
 
2.5%
132569409 1071
 
2.5%
159618145 1056
 
2.5%
161817865 1052
 
2.5%
155399513 1047
 
2.4%
147506619 1040
 
2.4%
2377136 1000
 
2.3%
Other values (9) 8767
 
20.4%
ValueCountFrequency (%)
0 24607
57.3%
2377136 1000
 
2.3%
6767836 1000
 
2.3%
18152566 993
 
2.3%
31918680 996
 
2.3%
47021991 1081
 
2.5%
75653853 1095
 
2.6%
102009213 1096
 
2.6%
132569409 1071
 
2.5%
147506619 1040
 
2.4%
ValueCountFrequency (%)
179700733 968
2.3%
178961451 967
2.3%
177742670 958
2.2%
176477289 962
2.2%
173123497 960
2.2%
165862664 963
2.2%
161817865 1052
2.5%
159618145 1056
2.5%
155399513 1047
2.4%
147506619 1040
2.4%

imunizados
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37933742
Minimum0
Maximum1.6861332 × 108
Zeros24607
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:12:00.137043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q363188489
95-th percentile1.6370674 × 108
Maximum1.6861332 × 108
Range1.6861332 × 108
Interquartile range (IQR)63188489

Descriptive statistics

Standard deviation60841963
Coefficient of variation (CV)1.6039009
Kurtosis-0.18772457
Mean37933742
Median Absolute Deviation (MAD)0
Skewness1.2528246
Sum1.6278127 × 1012
Variance3.7017445 × 1015
MonotonicityIncreasing
2023-10-08T21:12:00.413921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 24607
57.3%
41846334 1096
 
2.6%
26720042 1095
 
2.6%
22453204 1081
 
2.5%
63188489 1071
 
2.5%
133989193 1056
 
2.5%
143424991 1052
 
2.5%
116321657 1047
 
2.4%
92043897 1040
 
2.4%
1867 1000
 
2.3%
Other values (9) 8767
 
20.4%
ValueCountFrequency (%)
0 24607
57.3%
1867 1000
 
2.3%
2023619 1000
 
2.3%
5307991 993
 
2.3%
15846254 996
 
2.3%
22453204 1081
 
2.5%
26720042 1095
 
2.6%
41846334 1096
 
2.6%
63188489 1071
 
2.5%
92043897 1040
 
2.4%
ValueCountFrequency (%)
168613317 968
2.3%
165820496 967
2.3%
163706735 958
2.2%
160491877 962
2.2%
154888087 960
2.2%
149835637 963
2.2%
143424991 1052
2.5%
133989193 1056
2.5%
116321657 1047
2.4%
92043897 1040
2.4%

tarifa
Real number (ℝ)

Distinct33299
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean630.54852
Minimum57.55
Maximum3108.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:12:00.721642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57.55
5-th percentile282.281
Q1453.78
median604.93
Q3765.0925
95-th percentile1070.6345
Maximum3108.9
Range3051.35
Interquartile range (IQR)311.3125

Descriptive statistics

Standard deviation253.25963
Coefficient of variation (CV)0.40164971
Kurtosis3.7750144
Mean630.54852
Median Absolute Deviation (MAD)155.245
Skewness1.1489931
Sum27058098
Variance64140.439
MonotonicityNot monotonic
2023-10-08T21:12:01.058399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
399.9 17
 
< 0.1%
589.9 11
 
< 0.1%
2199 10
 
< 0.1%
299.9 10
 
< 0.1%
239.9 10
 
< 0.1%
563.93 9
 
< 0.1%
209.9 9
 
< 0.1%
599.9 9
 
< 0.1%
2209 9
 
< 0.1%
339.9 8
 
< 0.1%
Other values (33289) 42810
99.8%
ValueCountFrequency (%)
57.55 1
< 0.1%
64.35 1
< 0.1%
68.9 2
< 0.1%
69.94 1
< 0.1%
69.95 1
< 0.1%
76.81 1
< 0.1%
76.83 1
< 0.1%
76.9 1
< 0.1%
78.5 1
< 0.1%
80.03 1
< 0.1%
ValueCountFrequency (%)
3108.9 1
< 0.1%
3089 1
< 0.1%
3085.9 1
< 0.1%
3004.25 1
< 0.1%
2995.9 1
< 0.1%
2891.6 1
< 0.1%
2638.9 1
< 0.1%
2382.57 1
< 0.1%
2253.64 1
< 0.1%
2225.2 1
< 0.1%

n_voos
Real number (ℝ)

Distinct1008
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.71467
Minimum1
Maximum4573
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.4 KiB
2023-10-08T21:12:01.601222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q147
median101
Q3195
95-th percentile418
Maximum4573
Range4572
Interquartile range (IQR)148

Descriptive statistics

Standard deviation157.52233
Coefficient of variation (CV)1.0810327
Kurtosis52.280438
Mean145.71467
Median Absolute Deviation (MAD)66
Skewness4.3178054
Sum6252908
Variance24813.283
MonotonicityNot monotonic
2023-10-08T21:12:02.147368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 651
 
1.5%
2 435
 
1.0%
3 309
 
0.7%
4 264
 
0.6%
53 244
 
0.6%
45 242
 
0.6%
29 242
 
0.6%
55 240
 
0.6%
6 238
 
0.6%
28 236
 
0.5%
Other values (998) 39811
92.8%
ValueCountFrequency (%)
1 651
1.5%
2 435
1.0%
3 309
0.7%
4 264
0.6%
5 229
 
0.5%
6 238
 
0.6%
7 215
 
0.5%
8 221
 
0.5%
9 205
 
0.5%
10 193
 
0.4%
ValueCountFrequency (%)
4573 1
< 0.1%
4407 1
< 0.1%
3134 1
< 0.1%
3083 1
< 0.1%
2536 1
< 0.1%
2460 1
< 0.1%
2372 1
< 0.1%
2362 1
< 0.1%
2333 1
< 0.1%
2230 1
< 0.1%

Interactions

2023-10-08T21:11:41.836231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:52.172029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:56.009967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:59.967655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:05.057337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:08.662866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:12.641139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:17.114968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:21.862949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:25.283808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:28.848805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:34.243961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:38.337869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:42.100650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:52.424939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:56.286650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:00.384807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:05.343144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:08.931684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:12.903449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:17.585001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:22.120091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:25.547683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:29.116255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:34.700540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:38.607414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:42.363432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:52.676269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:56.555107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:00.815263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:05.610414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:09.230597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:13.167725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:18.007970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:22.376154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:25.819144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:29.382129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:35.201809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:38.876548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:42.625178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:52.940997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:56.823566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:01.270037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:05.871634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:09.502052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:13.438284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:18.437013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:22.614124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:26.108074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:29.644423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:35.552154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:39.135934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:42.902805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:53.209930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:57.090512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:01.690082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:06.137888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:09.773921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:13.705723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:18.876566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:22.878216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:26.373870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:29.908079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:35.836771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:39.403206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:43.158269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:53.469265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:57.359061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:02.115519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:06.412226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:10.047592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:13.976415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:19.301014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:23.134329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:26.634869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:30.223943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:36.106243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:39.676832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:43.432477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:53.745416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:57.632883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:02.498280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:06.685446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:10.337816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:14.252385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:19.759816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:23.400100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:26.907848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:30.596419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:36.386488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:39.946550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:43.709463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:54.045285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:57.937199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:02.962476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:06.986971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:10.610546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:14.564137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:20.167132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:23.660962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:27.202214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:31.041295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:36.657788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:40.225730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:43.957016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:54.652631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:58.192038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:03.296578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:07.277270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:10.862051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:14.958025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:20.439269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:23.906476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:27.461658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:31.452545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:36.925599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:40.475536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:44.221617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:54.926768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:58.485349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:03.691419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:07.549423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:11.549161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:15.390127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:20.729732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:24.172769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:27.750195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:32.495709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:37.209965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:40.751208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:44.490745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:55.200264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:58.797224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:04.119393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:07.830025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:11.824387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:15.840943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:21.020222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:24.451798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:28.042893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:32.921187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:37.506774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:41.025042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:44.775502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:55.482667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:59.163449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:04.521006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:08.120549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:12.112744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:16.285962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:21.316307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:24.721015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:28.327599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:33.375872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:37.788013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:41.316705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:45.041768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:55.752819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:10:59.551951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:04.786771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:08.402500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:12.398692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:16.731214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:21.584776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:25.027545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:28.589875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:33.796730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:38.078863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-08T21:11:41.587005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-08T21:12:02.624352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
mesorigem_latorigem_longdestino_latdestino_longnovos_casoscasos_ativosnovas_mortesmortes_por_casosprimeira_doseimunizadostarifan_voosanomes_do_periodoorigemorigem_nomedestinodestino_nomeempresa
mes1.000-0.0040.001-0.0030.001-0.118-0.0600.0240.261-0.068-0.0680.0530.0600.1991.0000.0000.0000.0000.0000.090
origem_lat-0.0041.0000.240-0.084-0.0230.0010.0010.0010.0010.0010.0010.198-0.1110.0000.0001.0001.0000.0780.0780.056
origem_long0.0010.2401.000-0.024-0.0720.0010.0020.0010.0020.0040.0040.0090.0050.0000.0001.0001.0000.0840.0840.047
destino_lat-0.003-0.084-0.0241.0000.2400.0020.0010.0010.0020.0000.0000.184-0.1240.0000.0000.0780.0781.0001.0000.056
destino_long0.001-0.023-0.0720.2401.0000.0010.0020.0010.0020.0040.0040.009-0.0010.0000.0000.0840.0841.0001.0000.047
novos_casos-0.1180.0010.0010.0020.0011.0000.8710.9030.6570.5740.574-0.0800.0260.6571.0000.0000.0000.0000.0000.074
casos_ativos-0.0600.0010.0020.0010.0020.8711.0000.7330.5380.8570.8570.1380.0580.7951.0000.0000.0000.0000.0000.090
novas_mortes0.0240.0010.0010.0010.0010.9030.7331.0000.8090.3970.397-0.223-0.0150.5101.0000.0000.0000.0000.0000.089
mortes_por_casos0.2610.0010.0020.0020.0020.6570.5380.8091.0000.2340.234-0.248-0.0500.6941.0000.0000.0000.0000.0000.104
primeira_dose-0.0680.0010.0040.0000.0040.5740.8570.3970.2341.0001.0000.3190.0830.7191.0000.0000.0000.0000.0000.108
imunizados-0.0680.0010.0040.0000.0040.5740.8570.3970.2341.0001.0000.3190.0830.6971.0000.0000.0000.0000.0000.108
tarifa0.0530.1980.0090.1840.009-0.0800.138-0.223-0.2480.3190.3191.000-0.1070.2770.2180.0940.0940.0910.0910.087
n_voos0.060-0.1110.005-0.124-0.0010.0260.058-0.015-0.0500.0830.083-0.1071.0000.0620.0860.0820.0820.0790.0790.043
ano0.1990.0000.0000.0000.0000.6570.7950.5100.6940.7190.6970.2770.0621.0001.0000.0000.0000.0000.0000.163
mes_do_periodo1.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.2180.0861.0001.0000.0000.0000.0000.0000.200
origem0.0001.0001.0000.0780.0840.0000.0000.0000.0000.0000.0000.0940.0820.0000.0001.0001.0000.0650.0650.082
origem_nome0.0001.0001.0000.0780.0840.0000.0000.0000.0000.0000.0000.0940.0820.0000.0001.0001.0000.0650.0650.082
destino0.0000.0780.0841.0001.0000.0000.0000.0000.0000.0000.0000.0910.0790.0000.0000.0650.0651.0001.0000.082
destino_nome0.0000.0780.0841.0001.0000.0000.0000.0000.0000.0000.0000.0910.0790.0000.0000.0650.0651.0001.0000.082
empresa0.0900.0560.0470.0560.0470.0740.0900.0890.1040.1080.1080.0870.0430.1630.2000.0820.0820.0820.0821.000

Missing values

2023-10-08T21:11:45.501055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-08T21:11:46.781683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

anomesmes_do_periodoorigemorigem_nomeorigem_latorigem_longdestinodestino_nomedestino_latdestino_longempresanovos_casoscasos_ativosnovas_mortesmortes_por_casosprimeira_doseimunizadostarifan_voos
02019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBBRBRASÍLIA-15.87-47.92AZU0.000.000.000.000.000.00594.1646
12019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBBRBRASÍLIA-15.87-47.92GLO0.000.000.000.000.000.00638.88192
22019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBBRBRASÍLIA-15.87-47.92ONE0.000.000.000.000.000.00468.4689
32019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBBRBRASÍLIA-15.87-47.92TAM0.000.000.000.000.000.00549.27208
42019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBCFCONFINS-19.62-43.97AZU0.000.000.000.000.000.00943.51144
52019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBCFCONFINS-19.62-43.97GLO0.000.000.000.000.000.00726.4494
62019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBCFCONFINS-19.62-43.97ONE0.000.000.000.000.000.00576.0229
72019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBCFCONFINS-19.62-43.97TAM0.000.000.000.000.000.00532.08135
82019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBCTCURITIBA-25.53-49.18AZU0.000.000.000.000.000.00564.5880
92019.001.002019.0.1.0SBBEBELÉM-1.38-48.48SBCTCURITIBA-25.53-49.18GLO0.000.000.000.000.000.00633.11120
anomesmes_do_periodoorigemorigem_nomeorigem_latorigem_longdestinodestino_nomedestino_latdestino_longempresanovos_casoscasos_ativosnovas_mortesmortes_por_casosprimeira_doseimunizadostarifan_voos
429022022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBRFRECIFE-8.13-34.92TAM1338980.003583270.774740.000.02179700733.00168613317.00781.5564
429032022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBRJRIO DE JANEIRO-22.91-43.16AZU1338980.003583270.774740.000.02179700733.00168613317.00710.57223
429042022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBRJRIO DE JANEIRO-22.91-43.16GLO1338980.003583270.774740.000.02179700733.00168613317.00586.13323
429052022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBRJRIO DE JANEIRO-22.91-43.16TAM1338980.003583270.774740.000.02179700733.00168613317.00605.13334
429062022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBSPCONGONHAS-23.63-46.66AZU1338980.003583270.774740.000.02179700733.00168613317.00798.6274
429072022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBSPCONGONHAS-23.63-46.66GLO1338980.003583270.774740.000.02179700733.00168613317.00641.48415
429082022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBSPCONGONHAS-23.63-46.66TAM1338980.003583270.774740.000.02179700733.00168613317.00719.21429
429092022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBSVSALVADOR-12.91-38.32AZU1338980.003583270.774740.000.02179700733.00168613317.001382.5773
429102022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBSVSALVADOR-12.91-38.32GLO1338980.003583270.774740.000.02179700733.00168613317.001027.40114
429112022.006.002022.0.6.0SBVTVITÓRIA-20.26-40.29SBSVSALVADOR-12.91-38.32TAM1338980.003583270.774740.000.02179700733.00168613317.001122.9334